Title :
The subspace learning algorithm as a formalism for pattern recognition and neural networks
Author :
Oja, Erkki ; Kohonen, Teuvo
Author_Institution :
Dept. of Inf. Technol., Lappeenranta Univ. of Technol., Finland
Abstract :
Vector subspaces have been suggested for representations of structured information. In the theory of associative memory and associative information processing, the projection principle and subspaces are used in explaining the optimality of associative mappings and novelty filters. These formalisms seem to be very pertinent to neural networks, too. Based on these operations, the subspace method has been developed for a practical pattern-recognition algorithm. The method is reviewed, and some recent results on image analysis are given.<>
Keywords :
content-addressable storage; information theory; learning systems; neural nets; pattern recognition; associative information processing; associative mappings; associative memory; image analysis; neural networks; pattern recognition; subspace learning algorithm; vector subspace; Associative memories; Information theory; Learning systems; Neural networks; Pattern recognition;
Conference_Titel :
Neural Networks, 1988., IEEE International Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/ICNN.1988.23858
